Python State Machine Documentation Release 0.7.1 Fernando Macedo Jan 17, 2019
Contents 1 Python State Machine 3 1.1 Getting started.............................................. 3 2 Installation 9 2.1 Stable release............................................... 9 2.2 From sources............................................... 9 3 Usage 11 4 Contributing 13 4.1 Types of Contributions.......................................... 13 4.2 Get Started!................................................ 14 4.3 Pull Request Guidelines......................................... 15 4.4 Tips.................................................... 15 5 Credits 17 5.1 Development Lead............................................ 17 5.2 Contributors............................................... 17 5.3 Credits.................................................. 17 6 History 19 6.1 0.7.1 (2019-01-18)............................................ 19 6.2 0.7.0 (2018-04-01)............................................ 19 6.3 0.6.2 (2017-08-25)............................................ 19 6.4 0.6.1 (2017-08-25)............................................ 19 6.5 0.6.0 (2017-08-25)............................................ 19 6.6 0.5.1 (2017-07-24)............................................ 20 6.7 0.5.0 (2017-07-13)............................................ 20 6.8 0.4.2 (2017-07-10)............................................ 20 6.9 0.3.0 (2017-03-22)............................................ 20 6.10 0.2.0 (2017-03-22)............................................ 20 6.11 0.1.0 (2017-03-21)............................................ 20 7 Indices and tables 21 i
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Contents: Contents 1
2 Contents
CHAPTER 1 Python State Machine Python finite-state machines made easy. Free software: MIT license Documentation: https://python-statemachine.readthedocs.io. 1.1 Getting started To install Python State Machine, run this command in your terminal: $ pip install python-statemachine Define your state machine: from statemachine import StateMachine, State class TrafficLightMachine(StateMachine): green = State('Green', initial=true) yellow = State('Yellow') red = State('Red') slowdown = green.to(yellow) stop = yellow.to(red) go = red.to(green) You can now create an instance: >>> traffic_light = TrafficLightMachine() And inspect about the current state: 3
>>> traffic_light.current_state State('Green', identifier='green', value='green', initial=true) >>> traffic_light.current_state == TrafficLightMachine.green == traffic_light.green True For each state, there s a dinamically created property in the form is_<state.identifier>, that returns True if the current status matches the query: >>> traffic_light.is_green True >>> traffic_light.is_yellow False >>> traffic_light.is_red False Query about metadata: >>> [s.identifier for s in m.states] ['green', 'red', 'yellow'] >>> [t.identifier for t in m.transitions] ['go', 'slowdown', 'stop'] Call a transition: >>> traffic_light.slowdown() And check for the current status: >>> traffic_light.current_state State('Yellow', identifier='yellow', value='yellow', initial=false) >>> traffic_light.is_yellow True You can t run a transition from an invalid state: >>> traffic_light.is_yellow True >>> traffic_light.slowdown() Traceback (most recent call last):... LookupError: Can't slowdown when in Yellow. You can also trigger events in an alternative way, calling the run(<transition.identificer>) method: >>> traffic_light.is_yellow True >>> traffic_light.run('stop') >>> traffic_light.is_red True A state machine can be instantiated with an initial value: >>> machine = TrafficLightMachine(start_value='red') >>> traffic_light.is_red True 4 Chapter 1. Python State Machine
1.1.1 Models If you need to persist the current state on another object, or you re using the state machine to control the flow of another object, you can pass this object to the StateMachine constructor: >>> class MyModel(object):... def init (self, state):... self.state = state... >>> obj = MyModel(state='red') >>> traffic_light = TrafficLightMachine(obj) >>> traffic_light.is_red True >>> obj.state 'red' >>> obj.state = 'green' >>> traffic_light.is_green True >>> traffic_light.slowdown() >>> obj.state 'yellow' >>> traffic_light.is_yellow True 1.1.2 Callbacks Callbacks when running events: from statemachine import StateMachine, State class TrafficLightMachine(StateMachine): "A traffic light machine" green = State('Green', initial=true) yellow = State('Yellow') red = State('Red') slowdown = green.to(yellow) stop = yellow.to(red) go = red.to(green) def on_slowdown(self): print('calma, lá!') def on_stop(self): print('parou.') def on_go(self): print('valendo!') >>> stm = TrafficLightMachine() >>> stm.slowdown() Calma, lá! >>> stm.stop() Parou. >>> stm.go() Valendo! 1.1. Getting started 5
Or when entering/exiting states: from statemachine import StateMachine, State class TrafficLightMachine(StateMachine): "A traffic light machine" green = State('Green', initial=true) yellow = State('Yellow') red = State('Red') cycle = green.to(yellow) yellow.to(red) red.to(green) def on_enter_green(self): print('valendo!') def on_enter_yellow(self): print('calma, lá!') def on_enter_red(self): print('parou.') >>> stm = TrafficLightMachine() >>> stm.cycle() Calma, lá! >>> stm.cycle() Parou. >>> stm.cycle() Valendo! 1.1.3 Mixins Your model can inherited from a custom mixin to auto-instantiate a state machine. class CampaignMachineWithKeys(StateMachine): "A workflow machine" draft = State('Draft', initial=true, value=1) producing = State('Being produced', value=2) closed = State('Closed', value=3) add_job = draft.to.itself() producing.to.itself() produce = draft.to(producing) deliver = producing.to(closed) class MyModel(MachineMixin): state_machine_name = 'CampaignMachine' def init (self, **kwargs): for k, v in kwargs.items(): setattr(self, k, v) super(mymodel, self). init () def repr (self): return "{}({!r})".format(type(self). name, self. dict ) (continues on next page) 6 Chapter 1. Python State Machine
model = MyModel(state='draft') assert isinstance(model.statemachine, campaign_machine) assert model.state == 'draft' assert model.statemachine.current_state == model.statemachine.draft (continued from previous page) 1.1. Getting started 7
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CHAPTER 2 Installation 2.1 Stable release To install Python State Machine, run this command in your terminal: $ pip install python-statemachine This is the preferred method to install Python State Machine, as it will always install the most recent stable release. If you don t have pip installed, this Python installation guide can guide you through the process. 2.2 From sources The sources for Python State Machine can be downloaded from the Github repo. You can either clone the public repository: $ git clone git://github.com/fgmacedo/python-statemachine Or download the tarball: $ curl -OL https://github.com/fgmacedo/python-statemachine/tarball/master Once you have a copy of the source, you can install it with: $ python setup.py install 9
10 Chapter 2. Installation
CHAPTER 3 Usage To use Python State Machine in a project: import statemachine 11
12 Chapter 3. Usage
CHAPTER 4 Contributing Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given. You can contribute in many ways: 4.1 Types of Contributions 4.1.1 Report Bugs Report bugs at https://github.com/fgmacedo/python-statemachine/issues. If you are reporting a bug, please include: Your operating system name and version. Any details about your local setup that might be helpful in troubleshooting. Detailed steps to reproduce the bug. 4.1.2 Fix Bugs Look through the GitHub issues for bugs. Anything tagged with bug and help wanted is open to whoever wants to implement it. 4.1.3 Implement Features Look through the GitHub issues for features. Anything tagged with enhancement and help wanted is open to whoever wants to implement it. 13
4.1.4 Write Documentation Python State Machine could always use more documentation, whether as part of the official Python State Machine docs, in docstrings, or even on the web in blog posts, articles, and such. 4.1.5 Submit Feedback The best way to send feedback is to file an issue at https://github.com/fgmacedo/python-statemachine/issues. If you are proposing a feature: Explain in detail how it would work. Keep the scope as narrow as possible, to make it easier to implement. Remember that this is a volunteer-driven project, and that contributions are welcome :) 4.2 Get Started! Ready to contribute? Here s how to set up python-statemachine for local development. 1. Fork the python-statemachine repo on GitHub. 2. Clone your fork locally: $ git clone git@github.com:your_name_here/python-statemachine.git 3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development: $ mkvirtualenv python-statemachine $ cd python-statemachine/ $ python setup.py develop 4. Create a branch for local development: $ git checkout -b name-of-your-bugfix-or-feature Now you can make your changes locally. 5. When you re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox: $ flake8 statemachine tests $ python setup.py test or py.test $ tox To get flake8 and tox, just pip install them into your virtualenv. 6. Commit your changes and push your branch to GitHub: $ git add. $ git commit -m "Your detailed description of your changes." $ git push origin name-of-your-bugfix-or-feature 7. Submit a pull request through the GitHub website. 14 Chapter 4. Contributing
4.3 Pull Request Guidelines Before you submit a pull request, check that it meets these guidelines: 1. The pull request should include tests. 2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst. 3. The pull request should work for Python 2.7, 3.3, 3.4 and 3.5. Check https://travis-ci.org/fgmacedo/ python-statemachine/pull_requests and make sure that the tests pass for all supported Python versions. 4.4 Tips To run a subset of tests: $ py.test tests.test_statemachine 4.3. Pull Request Guidelines 15
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CHAPTER 5 Credits 5.1 Development Lead Fernando Macedo <fgmacedo@gmail.com> 5.2 Contributors Guilherme Nepomuceno <piercio@loggi.com> 5.3 Credits This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template. 17
18 Chapter 5. Credits
CHAPTER 6 History 6.1 0.7.1 (2019-01-18) Fix Django integration for registry loading statemachine modules on Django1.7+. 6.2 0.7.0 (2018-04-01) New event callbacks: on_enter_<state> and on_exit_<state>. 6.3 0.6.2 (2017-08-25) Fix README. 6.4 0.6.1 (2017-08-25) Fix deploy issues. 6.5 0.6.0 (2017-08-25) Auto-discovering statemachine/statemachines under a Django project when they are requested using the mixin/registry feature. 19
6.6 0.5.1 (2017-07-24) Fix bug on CombinedTransition._can_run not allowing transitions to run if there are more than two transitions combined. 6.7 0.5.0 (2017-07-13) Custom exceptions. Duplicated definition of on_execute callback is not allowed. Fix bug on StateMachine.on_<transition.identifier> being called with extra self param. 6.8 0.4.2 (2017-07-10) Python 3.6 support. Drop official support for Python 3.3. Transition can be used as decorator for on_execute callback definition. Transition can point to multiple destination states. 6.9 0.3.0 (2017-03-22) README getting started section. Tests to state machine without model. 6.10 0.2.0 (2017-03-22) State can hold a value that will be assigned to the model as the state value. Travis-CI integration. RTD integration. 6.11 0.1.0 (2017-03-21) First release on PyPI. 20 Chapter 6. History
CHAPTER 7 Indices and tables genindex modindex search 21